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Prediksi Prestasi Akademik Mahasiswa Bekerja Paruh Waktu Menggunakan Artificial Neural Network
Abstract
Intisari–Mahasiswa yang bekerja paruh waktu dituntut agar bisa membagi waktu mereka secara efektif dan efisien antara waktu untuk bekerja dan dan waktu untuk kuliah. Prediksi terhadap mereka yang kuliah sambil bekerja diharapkan dapat menjadi salah satu pertimbangan kebijakan bagi pihak akademik agar mahasiswa yang bekerja sambil bekerja dapat menyelesaikan masa studi mereka secara tepat waktu. Penelitian ini di mulai dengan tahapan mengumpulkan data mahasiswa yang kuliah sambil bekerja untuk selanjutnya dilakukan proses data cleaning. Data lalu dibagi atas dua kelompok data yaitu data training dan data testing yang dinormalisasi dengan metode min-max. Algoritma neural network digunakan untuk melakukan prediksi terhadap hasil studi bagi mereka yang kuliah sambil bekerja yang di kategorikan dalam 3 label. Optimasi dilakukan terhadap parameter dengan memamfaatkan perangkat optimize parameter. Pada pengujian model, parameter yang ditampilkan berupa training cycle, learning rate, momentum, akurasi dan nilai RMSE dengan rentang nilai learning rate dan momentum 0,1 sampai dengan 0,9, dengan fungsi aktivasi sigmoid. Validasi nilai terbaik didapat pada training cycle 201, learning rate 0,74, momentum 0,9 dengan nilai akurasi 89,62%, RMSE 0,263 dengan nilai k-fold=3.
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DOI: https://doi.org/10.35314/isi.v7i1.2368
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